R Notebook

Libraries

library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
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  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
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✔ tibble  3.1.7     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1
── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(scales)

Attaching package: ‘scales’

The following object is masked from ‘package:purrr’:

    discard

The following object is masked from ‘package:readr’:

    col_factor

library(plotly)

Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

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    filter

The following object is masked from ‘package:graphics’:

    layout

library(lubridate)

Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

    date, intersect, setdiff, union

library(sf)

Linking to GEOS 3.10.2, GDAL 3.4.2, PROJ 8.2.1; sf_use_s2() is TRUE

library(zoo)

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric

Covid Tab

#load in beds and add year column
beds <- read_csv("raw_data/non_covid_raw_data/beds_by_nhs_board_of_treatment_and_specialty.csv") %>% janitor::clean_names()

beds <- beds %>% 
mutate(quarter_date = yq(quarter),
       year = year(quarter_date), 
       .after = quarter)

beds %>% 
write_csv("clean_data/non_covid_data/beds.csv")

Plot for bed availability for all acute

# bed percentage availablity for "all acute"
# Will need to add filter for year based on user input
beds_plotly <- beds %>%
  filter(specialty_name == "All Acute") %>% 
  group_by(quarter, specialty_name) %>%
  summarise(mean_perc_occ = mean(percentage_occupancy)) %>% 
  ggplot(aes(x = quarter, y = mean_perc_occ))+
  geom_line(aes(colour = specialty_name, group = specialty_name))+
  geom_point(aes(
    text = paste0("Occupancy: ",round(mean_perc_occ, digits = 2),"%<br>","Date: ", quarter)),
    size = 0.5)+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  labs(title = "Mean bed availability for all Acute Patients",
       x = "\nYear and Quarter",
       y = "Average Percentage Occupancy")

ggplotly(beds_plotly, tooltip = "text") %>% 
  config(displayModeBar = FALSE) %>% 
  layout(hoverlabel=list(bgcolor="red"))

Covid AE attendance graphs

#shows number of attendances at A&E during covid
# Title = Number of attendances at A&E 2020 - 2022
covid_ae_attendance_plotly <- covid_ae_attendance %>% 
  filter(hb_name == "All Scotland") %>% 
  ggplot() +
  geom_point(aes(x = date,
                 y = num_attendances,
                 text = paste0("Date: ", year, "-", month,
                              "<br>",
                   "Number of admissions: ", num_attendances,
                 "<br>",
                 "2017-2019 avg admissions: ", 
                 round(avg_attendances_20171819))
                 )) +
  geom_line(aes(x = date,
                y = num_attendances)) +
  geom_line(aes(x = date,
                y = avg_attendances_20171819), 
            colour = "grey60", alpha = 0.5) +
  scale_x_date(date_breaks = "3 months", date_labels = "%b %Y") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, size =7)) +
   geom_vline(xintercept = as.numeric(as.Date("2020-01-01")), linetype=4, colour = "grey50")+
  geom_vline(xintercept = as.numeric(as.Date("2021-01-01")), linetype=4, colour = "grey50")+
  geom_vline(xintercept = as.numeric(as.Date("2022-01-01")), linetype=4, colour = "grey50")+
  labs(title = "Comparison with 2018-2019 averages \n",
       x = "Date",
       y = "Number of Attendances")

covid_ae_attendance_plotly %>% 
  ggplotly(tooltip = "text") %>% 
  config(displayModeBar = FALSE) %>% 
  layout(hoverlabel = list(bgcolor = "white"))

#shows destination breakdown for A&E attendances during covid
#Title: Destination of attendances at A&E 2020 - 2022
covid_ae_destinations_plotly <- covid_ae_attendance %>% 
  filter(hb_name == "All Scotland") %>% 
  ggplot() +
  geom_point(aes(x = date,
                 y = destination_prop,
                 colour = destination,
                 text = paste0("Date: ", year, "-", month,
                               "<br>",
                               "Percentage: ", 
                              round(destination_prop*100, digits = 2), 
                              "%",
                              "<br>",
                              "2017-2019 percentage: ", 
                              round(avg_prop_20171819*100, digits = 2), 
                              "%"))) +
  geom_line(aes(x = date,
                 y = destination_prop,
                group = destination)) +
   geom_line(aes(x = date,
                 y = avg_prop_20171819,
                 colour = destination,
                group = destination), alpha = 0.5) +
  scale_x_date(date_breaks = "3 months", date_labels = "%b %Y") +
  scale_y_sqrt() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, size =7)) +
   geom_vline(xintercept = as.numeric(as.Date("2020-01-01")), linetype=4, colour = "grey50")+
  geom_vline(xintercept = as.numeric(as.Date("2021-01-01")), linetype=4, colour = "grey50")+
  geom_vline(xintercept = as.numeric(as.Date("2022-01-01")), linetype=4, colour = "grey50")+
  labs(title = "Comparison with proportion of attendances at A&E 2017 - 2019 \n",
       x = "Date",
       y = "Proportion of attendances",
       colour = "Destination")
covid_ae_destinations_plotly %>% 
  ggplotly(tooltip = "text") %>% 
  config(displayModeBar = FALSE)

Scotland Shapefile

ae_wait_times wrangling

ae_wait_times <- read_csv("raw_data/non_covid_raw_data/monthly_ae_waitingtimes_202206.csv") %>% janitor::clean_names()

#make a date and year column with the first date of every month
ae_wait_times <- ae_wait_times %>% 
  mutate(date = ym(month), .after = month,
         year = year(date))

#make a percent column with percent of patients meeting the 4hr target time
ae_wait_times <- ae_wait_times %>% 
  mutate(percent_4hr_target_achieved = (number_meeting_target_aggregate/number_of_attendances_aggregate)*100)

ae_wait_times %>% 
write_csv("clean_data/non_covid_data/ae_wait_times.csv")

add in target data for the shape files

#write in the target data for the shapefile colours
target_2007 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2007) %>% 
  rename(ae_target_2007 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2007)
  

target_2008 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>%
  filter(year == 2008) %>% 
  rename(ae_target_2008 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2008)

target_2009 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2009) %>% 
  rename(ae_target_2009 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  selecat(hbt,ae_target_2009)

target_2010 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2010) %>% 
  rename(ae_target_2010 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2010)

target_2011 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2011) %>% 
  rename(ae_target_2011 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2011)

target_2012 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2012) %>% 
  rename(ae_target_2012 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2012)

target_2013 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2013) %>% 
  rename(ae_target_2013 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2013)

target_2014 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved =
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2014) %>% 
  rename(ae_target_2014 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2014)

target_2015 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2015) %>% 
  rename(ae_target_2015 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2015)

target_2016 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2016) %>% 
  rename(ae_target_2016 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2016)

target_2017 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2017) %>% 
  rename(ae_target_2017 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2017)

target_2018 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2018) %>% 
  rename(ae_target_2018 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2018)

target_2019 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2019) %>% 
  rename(ae_target_2019 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2019)

target_2020 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2020) %>% 
  rename(ae_target_2020 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2020)

target_2021 <- ae_wait_times %>%
  group_by(year, hbt) %>% 
  summarise(ae_4hr_target_achieved = 
              round(mean(percent_4hr_target_achieved, na.rm = TRUE), digits = 2)) %>% 
  filter(year == 2021) %>% 
  rename(ae_target_2021 = ae_4hr_target_achieved) %>% 
  ungroup() %>% 
  select(hbt,ae_target_2021)
  

shape file wrangling

scotland <- st_read("../SG_NHS_HealthBoards_2019_shapefile/SG_NHS_HealthBoards_2019.shp")

# make a smaller version for performance issues
scotland_smaller <- scotland %>% 
  st_simplify(TRUE, dTolerance = 2000)
#fixes problems caused by above 
scotland_smaller <- sf::st_cast(scotland_smaller, "MULTIPOLYGON")

# centres <-  scotland_smaller %>% 
#   mutate(centres = st_centroid(st_make_valid(geometry))) %>%
#     mutate(lat = st_coordinates(centres)[,1],
#            long = st_coordinates(centres)[,2])a

#add in the A&E 4 hr target data for each year
scotland_smaller <-  scotland_smaller %>% 
    mutate(target_2007 = target_2007$ae_target_2007,
           target_2008 = target_2008$ae_target_2008,
           target_2009 = target_2009$ae_target_2009,
           target_2010 = target_2010$ae_target_2010,
           target_2011 = target_2011$ae_target_2011,
           target_2012 = target_2012$ae_target_2012,
           target_2013 = target_2013$ae_target_2013,
           target_2014 = target_2014$ae_target_2014,
           target_2015 = target_2015$ae_target_2015,
           target_2016 = target_2016$ae_target_2016,
           target_2017 = target_2017$ae_target_2017,
           target_2018 = target_2018$ae_target_2018,
           target_2019 = target_2019$ae_target_2019,
           target_2020 = target_2020$ae_target_2020,
           target_2021 = target_2021$ae_target_2021
                  )

# save the map
st_write(scotland_smaller, "clean_data/shapefile/scotland_smaller.gpkg", append = FALSE) 

## reload the map
scotland_smaller <- st_read("clean_data/shapefile/scotland_smaller.gpkg") 

# # This will require filtered by the year selected in the dashboard
# # Can we get the button or dropdown to pass eg "target_2016" to this in 2 places?

p <- ggplot(scotland_smaller) + 
  geom_sf(aes(fill = target_2021, 
              text = paste("<b>", HBName, "</b>\n", 
                           round(target_2021, digits = 2),"%", sep = ""))) + 
  scale_fill_viridis_c(option = "plasma", name = "4Hr A&E Target %")+
  theme_void()+
  labs(title = "Percent of A&E depts making the 4hr target")

p %>%
  ggplotly(tooltip = "text") %>%
  style(hoverlabel = list(bgcolor = "white"), hoveron = "fill")%>% 
  config(displayModeBar = FALSE)

SIMD tab

SIMD episodes line graph

simd <- read_csv("raw_data/non_covid_raw_data/inpatient_and_daycase_by_nhs_board_of_treatment_and_simd.csv") %>% janitor::clean_names()

simd <- simd %>% 
mutate(quarter_date = yq(quarter),
       year = year(quarter_date), 
       .after = quarter)

simd %>% 
write_csv("clean_data/non_covid_data/simd.csv")

# average episodes by SIMD value
# currently unfiltered for health board or admission type etc
simd_plotly <- simd %>% 
  drop_na(simd) %>%
  mutate(simd = as.factor(simd)) %>% # gives each simd a separate colour
  group_by(quarter, simd) %>% 
  summarise(avg_episodes = mean(episodes, na.rm = TRUE)) %>% 
  ggplot(aes(x = quarter, y = avg_episodes, group = simd))+
  geom_line(aes(colour = simd))+
  geom_point(aes(text = paste0("Date: ", quarter, "<br>",
                               "Average Episodes: ", round(avg_episodes, digits = 2), "<br>",
                               "SIMD: ", simd),
                   colour = simd),size = 0.5)+
  scale_y_continuous(labels = scales::comma)+
  labs(title = "Average Hospital Episodes by SIMD Deprivation score\n",
       x = "\nYear and Quarter",
       y = "Average Episodes\n")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ggplotly(simd_plotly, tooltip = "text") %>% 
  config(displayModeBar = FALSE) 

Age tab

Age episode line Graph

age_sex <- read_csv("raw_data/non_covid_raw_data/inpatient_and_daycase_by_nhs_board_of_treatment_age_and_sex.csv") %>% janitor::clean_names()

age_sex <- age_sex %>%
    mutate(quarter_date = yq(quarter),
           year = year(quarter_date),
           .after = quarter)

age_sex %>% 
  write_csv("clean_data/non_covid_data/age_sex.csv")

# Average number of episode for age groups
# currently unfiltered by department or anything else
age_plotly <- age_sex %>% 
  #filter(min_date < year & year < max_date) %>% 
  group_by(quarter, age) %>% 
  summarise(avg_episodes = mean(episodes, na.rm = TRUE)) %>% 
  ggplot(aes(x = quarter, y = avg_episodes))+
  geom_line(aes(colour = age, group = age))+ 
  geom_point(aes(colour = age,
                 text = paste0("Date: ", quarter, "<br>",
                               "Average Episodes: ", round(avg_episodes, digits = 2), "<br>",
                               "Age Group: ", age)),size = 0.5)+
  labs(title = "Average Hospital Episodes by Age Groups\n",
       x = "\nYear and Quarter",
       y = "Average Episodes\n")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
  
ggplotly(age_plotly, tooltip = "text") %>% 
  config(displayModeBar = FALSE)

Age column chart

hb_agesex <- read_csv("clean_data/covid_agesex.csv")

Rows: 43516 Columns: 16
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (10): month, hb, hbqf, age_group, age_group_qf, sex, sex_qf, admission_type, admission_type_qf, hb_name
dbl   (4): year, number_admissions, average20182019, percent_variation
lgl   (1): is_winter
date  (1): week_ending

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

#title: Average admissions by age group 2020-2022
covid_age_plotly <- hb_agesex %>% 
  filter(admission_type == "Emergency",
         age_group != "All ages", 
         hb_name == "All Scotland") %>% 
  group_by(hb_name, age_group, is_winter) %>% 
  summarise(mean_admissions = mean(number_admissions),
            mean_20182019_admissions = mean(average20182019)) %>% 
  ggplot() +
  geom_col(aes(x = ordered(age_group, levels = c("Under 5",
                                                 "5 - 14",
                                                 "15 - 44",
                                                 "45 - 64",
                                                 "65 - 74",
                                                 "75 - 84",
                                                 "85 and over")),
               y = mean_admissions, 
               fill = if_else(is_winter == TRUE,
                              "Winter", "Not winter"),
               text = paste0("Age group: ", age_group,
                             "<br>",
                             "Average number of admissions: ", 
                             round(mean_admissions),
                             "<br>",
                             "2018/2019 avg admissions: ", 
                             round(mean_20182019_admissions))), 
           position = "dodge") +
  labs(title = "Comparison between winter and non-winter months",
       x = "\n Age group",
       y = "Mean number of admissions",
       fill = "Season")

`summarise()` has grouped output by 'hb_name', 'age_group'. You can override using the `.groups` argument.

Warning: Ignoring unknown aesthetics: text

covid_age_plotly %>% 
  ggplotly(tooltip = "text") %>% 
  config(displayModeBar = FALSE) 

Sex tab

Sex line graph

Scotland leaflet map

library(leaflet)
library(sf)
# scotland_smaller <-  readOGR("../SG_NHS_HealthBoards_2019_shapefile/",layer = "SG_NHS_HealthBoards_2019")
# shapeData <- spTransform(scotland_smaller, CRS("+proj=longlat +ellps=GRS80"))
# scotland <-  scotland %>% 
#   mutate(centres = st_centroid(st_make_valid(geometry))) %>%
#     mutate(lat = st_coordinates(centres)[,1],
#            long = st_coordinates(centres)[,2])
library(here)
here()
#read the geo package in
scotland = st_read("clean_data/shapefile/scotland_smaller.gpkg")

#transform so leaflet is happy with it
scotland <- st_transform(scotland, '+proj=longlat +datum=WGS84')

pal <- colorNumeric("viridis", NULL) # set colour palette

# open up leaflet and add scotland as data, along with other variables
m <- leaflet() 
m %>% addTiles() %>% 
  addPolygons(data=scotland,
              smoothFactor = 0.3, 
              fillOpacity = 1,
              fillColor = ~pal(target_2007),
              label = ~paste0( HBName,": ", target_2007),
              weight = 1, 
              highlightOptions = highlightOptions(color = "white", 
                                                            weight = 2, 
                                                            bringToFront = TRUE)) %>% 
  addLegend(pal = pal, values = scotland$target_2007, opacity = 1)

Winter tab

A&E destination breakdown

#read in clean dataset
waiting_times <- read_csv("clean_data/wait_times.csv")

#graph of A&E destination breakdown
winter_plotly <- waiting_times %>% 
  filter(discharge_destination == "Admission to Same Facility",
         !is.na(discharge_proportion)) %>% 
  group_by(date) %>% 
  summarise(mean_admission_to_same = mean(discharge_proportion)) %>% 
  ggplot() +
  geom_point(aes(x = date,
         y = mean_admission_to_same,
         text =  paste0("Date: ", date,
                               "<br>",
                               "Percentage: ", 
                              round(mean_admission_to_same*100, digits = 2), 
                              "%"))) +
  geom_line(aes(x = date,
         y = mean_admission_to_same)) +
  scale_x_date(date_breaks = "6 months", date_labels =  "%b %Y") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, size =7)) +
  geom_vline(xintercept = as.numeric(as.Date("2008-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2009-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2010-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2011-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2012-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2013-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2014-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2015-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2016-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2017-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2018-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2019-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2020-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2021-01-01")), linetype=4, colour = "grey50", alpha = 0.7)+
  geom_vline(xintercept = as.numeric(as.Date("2022-01-01")), linetype=4, colour = "grey50", alpha = 0.7) +
  labs(title = "Proportion of attendances to selected destination \n",
       x = "\n Date",
       y = "Proportion of attendances")

winter_plotly %>% 
  ggplotly(tooltip = "text") %>% 
  config(displayModeBar = FALSE) %>% 
layout(hoverlabel = list(bgcolor = "white"))